Oklahoma City MSA — Travel Demand Model

Data Overview

The following data was aggregated and computed from the 2021 ACS 5-year estimate, and Longitudinal Employer-Household Dynamica dataset. An overview of the resulting dataset is provided below:

Data Structure:

The sample table below shows the first 5 rows of our compiled dataset. Each row represents a census tract in the study area along with its corresponding variables and geometry. When necessary, this dataset can easily be grouped by county, pivoted by category, or mapped geographically.

## # A tibble: 5 × 40
##   GEOID    NAME  tot_p…¹ no_vehE total…² hh_1p…³ hh_2p…⁴ hh_3p…⁵ hh_4p…⁶ hh_u1…⁷
##   <chr>    <chr>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
## 1 4002720… Cens…    4371       6    1540     334     509     167     530    1028
## 2 4010910… Cens…    2043      22     702     199     131     157     215     302
## 3 4010910… Cens…     581       0       8       0       8       0       0       0
## 4 4002720… Cens…    4021       0    2019     795     794     255     175     320
## 5 4002720… Cens…    3238       9    1370     534     436     164     236     505
## # … with 30 more variables: hh_u18_singleparent_maleE <dbl>,
## #   hh_u18_singleparent_femaleE <dbl>, hh_65plusE <dbl>, tot_disabledE <dbl>,
## #   inc_lt_10kE <dbl>, inc_btw_10k_15kE <dbl>, inc_btw_15k_20kE <dbl>,
## #   inc_btw_20k_25kE <dbl>, inc_btw_25k_30kE <dbl>, inc_btw_30k_35kE <dbl>,
## #   inc_btw_35k_40kE <dbl>, inc_btw_40k_45kE <dbl>, inc_btw_45k_50kE <dbl>,
## #   inc_btw_50k_60kE <dbl>, inc_btw_60k_75kE <dbl>, inc_btw_75k_100kE <dbl>,
## #   inc_btw_100k_125kE <dbl>, inc_btw_125k_150kE <dbl>, …

Data Definitions:

GEOID - Numerical ID for census tract
NAME - Census tract, county, and state
tot_popE - Total number of people
no_vehE - Households with no vehicle present
total_hhsE - Total number of households
hh_1personE - Number of households with 1 person
hh_2personE - Number of households with 2 persons
hh_3personE - Number of households with 3 persons
hh_4person_plusE - Number of households with 4 or more persons
hh_u18_married_coupleE - Number of households with a person under 18 and a married couple head-of-household
hh_u18_singleparent_maleE - Number of households with a person under 18 and a single male head-of-household
hh_u18_singleparent_femaleE - Number of households with a person under 18 and a single female head-of-household
hh_65plusE - Number of households with a person age 65 or older
tot_disabledE - Number of people with a disability
inc_lt_10kE - Number of households with income less than $10,000
inc_btw_10k_15kE - Households with income between $10,000 and $15,000
inc_btw_15k_20kE - Households with income between $15,000 and $20,000
inc_btw_20k_25kE - Households with income between $20,000 and $25,000
inc_btw_25k_30kE - Households with income between $25,000 and $30,000
inc_btw_30k_35kE - Households with income between $30,000 and $35,000
inc_btw_35k_40kE - Households with income between $35,000 and $40,000
inc_btw_40k_45kE - Households with income between $40,000 and $45,000
inc_btw_45k_50kE - Households with income between $45,000 and $50,000
inc_btw_50k_60kE - Households with income between $50,000 and $60,000
inc_btw_60k_75kE - Households with income between $60,000 and $75,000
inc_btw_75k_100kE - Households with income between $75,000 and $100,000
inc_btw_100k_125kE - Households with income between $100,000 and $125,000
inc_btw_125k_150kE - Households with income between $125,000 and $150,000
inc_btw_150k_200kE - Households with income between $150,000 and 200,000
inc_gt_200kE - Households with income greater than $200,000
hh_povlevelE - Number of households below the poverty level
total_emp - Total number of people employed
basic_emp - Total number of people employed in the following sectors: Agriculture, Forestry, Fishing, and Hunting (CNS01) Mining and extraction (CNS02) Utilities (CNS03) Construction (CNS04) Manufacturing (CNS05) Wholesale trade (CNS06) Transportation and warehousing (CNS06)
retail_emp - Total number of people employed in retail
service_emp - Total number of people employed in remaining sectors
land_area_sqmeters - Land area in square meters
geometry - Geographic coordinates of census tract outlines
pop_density - People per square meter (calculated)
emp_density - Employees per square meter (calculated)
activity_density - People and employees per square meter (calculated)

Geography Overview

The Oklahoma City metropolitan statistical area (MSA) is composed of seven counties centrally located in Oklahoma: Canadian County, Cleveland County, Grady County, Lincoln County, Logan County, McClain County, and Oklahoma County. Across these counties there are a total of 363 census tracts (based on the 2020 redistricting). The MSA has a total land area of approximately 1,427,523 square kilometers.Together, these counties are home to 1,412,900 people, according to the 2021 ACS 5-Year Estimates. The Oklahoma City MSA makes up nearly 36% of the state’s total population of 3,948,100.

The Oklahoma City MSA is predominantly white, with 63% of residents identifying as white alone, followed by 13.9% Hispanic or Latino, 10% Black, 3.1% American Indian and Alaska Native, 3.1% Asian, 0.1% Native Hawaiian and Pacific Islander. 37% of residents in the study are are ages 35-64, 24.6% are under the age of 18, 24.4% are ages 18-34, and 14% are ages 65 and older. The median household income in 2021 was $63,351 – higher than the statewide average and lower than the national average. Just over 14% of residents live below the poverty line – lower than the statewide average and higher than the national average.

Transit

EMBARK, the area’s transit authority, operates all public transit in greater Oklahoma City, which includes fixed-route bus service, the OKC Streetcar, paratransit service, river ferry transit, and a bikeshare network. Beyond transit, car-ownership and use is prevalent in the Oklahoma City MSA with only 2.6% of households reporting that they do not have access to a vehicle, and a median number of vehicles per household of 2.3. According to the 2021 ACS 5-Year Estimates, cars, trucks, or vans are the most common means of transportation to work for workers 16 and over (used by 89.7% of respondents), while commuting by public transportation is far less common (used by 0.5% of respondents). Based on the same ACS data, the average travel time to work is 23 minutes.

Employment

Major employment sectors in the Oklahoma City metro area include government, higher education, aerospace, healthcare, and retail. According to the Greater Oklahoma City Chamber of Commerce, major employers include the State of Oklahoma, Tinker Air Force Base, the University of Oklahoma, Integris Health, and Amazon. In 2021, the metropolitan area added 10,825 jobs (1.7% increase), and further job growth was expected in 2022 as stated in the 2022 Greater Oklahoma City Economic Outlook.

Map showing the counties and census tracts in the Oklahoma City MSA

Densities

Map showing the population density in the Oklahoma City MSA

Map showing the employment density in the Oklahoma City MSA

Employment & Income

Employment by sector by county

Distribution of households by income bracket

Map showing the spatial distribution of poverty in the Oklahoma City MSA

Vehicle Ownership

Map showing the spatial distribution of vehicle access in the Oklahoma City MSA

Distribution of cencus tracts by percent of households with access to a vehicle

Travel Time Matrix: Roads

To create the network in TransCAD, we imported the Open Street Maps layer and selected for primary, secondary, tertiary, and trunk roads. When creating the centroids and centroid connectors, we allowed for the connectors to extend outside of zone boundaries, at up to 30 miles, and up to 10 connectors per centroid. This resulted in a matrix with only a few gaps, which we resolved by turning surrounding roads two-way. (The major roads in the area were separated highways, so we trust that two-way driving is actually feasible in these zones.)

Isochrones

After generating a complete matrix, we read the travel time matrix and the nodes layer into R. We converted the centroids’ coordinates to a geometry, matched centroid IDs with the appropriate census tracts, and pivoted the travel time matrix so origins and destinations would both be in columns. This allowed us to create an isochrone illustrating the travel time from one zone in the study area to all others.

Accessibility Score

This metric allows us to compare the relative accessibility of tracts more easily. We calculated an “accessibility score” by counting the number of zones each origin could reach within 30 minutes. We note that this score is affected by the lack of residential roads in our network, which would likely provide better accessibility for some of the peripheral zones.

Travel Time Matrix: Transit

Assumptions:

In our model and in this house we assume: everyone walks to transit, vehicle speed is 23 mph, walking speed is 2.5 mph, 15-minute wait time, fare is 2 dollars and it is free to transfer.

Plotting EMBARK Routes

This includes both bus routes and a streetcar route that we coded as rail.

Transit Isochrones and Travel Time Break Down

Advantage of public transit data is break down the overall trip and travel time to produce a more granular analysis. The shape of the first charts are similar, demonstrating similar travel patterns in the downtown region. The third chart illustrates a much steeper increase in travel time. We ordered the stacked bar chart from shortest to longest amount of time rather than geographic distance. It is interesting to compare the three stacked bar charts. The first two follow a more exponential trend while the third follows more of a logarithmic trend.